first commit

test.py

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+import mlx.core as mx
+import numpy as np
+# Functions shared by both the training and the testing scripts.
+from common import *
+
+# Hyperparameters.
+
+HIDDEN_SIZE = 256
+HIDDEN_LAYERS = 4
+
+# Testing images. It's a numpy array of size 10000x784.
+testing = load_images("dataset/t10k-images-idx3-ubyte/t10k-images-idx3-ubyte")
+labels = load_labels("dataset/t10k-labels-idx1-ubyte/t10k-labels-idx1-ubyte")
+
+# Load parameters from training results.
+W1 = mx.array(np.loadtxt("weights/weights1.txt", dtype=np.float32))
+b1 = mx.array(np.loadtxt("biases/biases1.txt", dtype=np.float32))
+
+W_hidden = []
+b_hidden = []
+for i in range(HIDDEN_LAYERS):
+    W_hidden.append(mx.array(np.loadtxt(f"weights/weights_hidden{i}.txt", dtype=np.float32)))
+    b_hidden.append(mx.array(np.loadtxt(f"biases/biases_hidden{i}.txt", dtype=np.float32)))
+
+W_out = mx.array(np.loadtxt("weights/weights3.txt", dtype=np.float32))
+b_out = mx.array(np.loadtxt("biases/biases3.txt", dtype=np.float32))
+
+images = mx.array(testing)
+labels = mx.array(labels)
+
+z1 = linear(images, W1, b1)
+# Apply activation.
+a1 = relu(z1)
+
+# Hidden layers regression and activation.
+z_hidden = [linear(a1, W_hidden[0], b_hidden[0])]
+a_hidden = [relu(z_hidden[0])]
+for l in range(1, HIDDEN_LAYERS):
+    z_hidden.append(linear(a_hidden[-1], W_hidden[l], b_hidden[l]))
+    a_hidden.append(relu(z_hidden[l]))
+
+# Output layer regression.
+z_out = linear(a_hidden[-1], W_out, b_out)
+# Apply activation
+y_hat = softmax(z_out)
+# Transform the predictions to a vector of class indices containing the numerical label (0-9).
+predictions = mx.argmax(y_hat, axis=1)
+# Check how many predictions were correct.
+correct = mx.sum(predictions == labels)
+
+accuracy = (correct / labels.shape[0]) * 100
+print(f"Accuracy: {accuracy.item():.2f}%")